Course:

This tutorial teaches users how to create animations of BOLD responses over time, to allow researchers and clinicians to visualize time-course activity patterns.

Difficulty level: Intermediate

Duration: 12:52

Speaker: : Mike X. Cohen

Course:

This tutorial demonstrates how to use MATLAB to create event-related BOLD time courses from fMRI datasets.

Difficulty level: Intermediate

Duration: 13:39

Speaker: : Mike X. Cohen

Course:

In this tutorial, users learn how to compute and visualize a t-test on experimental condition differences.

Difficulty level: Intermediate

Duration: 17:54

Speaker: : Mike X. Cohen

Course:

This lesson introduces various methods in MATLAB useful for dealing with data generated by calcium imaging.

Difficulty level: Intermediate

Duration: 5:02

Speaker: : Mike X. Cohen

Course:

This tutorial demonstrates how to use MATLAB to generate and visualize animations of calcium fluctuations over time.

Difficulty level: Intermediate

Duration: 15:01

Speaker: : Mike X. Cohen

Course:

This tutorial instructs users how to use MATLAB to programmatically convert data from cells to a matrix.

Difficulty level: Intermediate

Duration: 5:15

Speaker: : Mike X. Cohen

Course:

In this tutorial, users will learn how to identify and remove background noise, or "blur", an important step in isolating cell bodies from image data.

Difficulty level: Intermediate

Duration: 17:08

Speaker: : Mike X. Cohen

Course:

This lesson teaches users how MATLAB can be used to apply image processing techniques to identify cell bodies based on contiguity.

Difficulty level: Intermediate

Duration: 11:23

Speaker: : Mike X. Cohen

Course:

This tutorial demonstrates how to extract the time course of calcium activity from each clusters of neuron somata, and store the data in a MATLAB matrix.

Difficulty level: Intermediate

Duration: 22:41

Speaker: : Mike X. Cohen

Course:

This lesson demonstrates how to use MATLAB to implement a multivariate dimension reduction method, PCA, on time series data.

Difficulty level: Intermediate

Duration: 17:19

Speaker: : Mike X. Cohen

This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.

Difficulty level: Intermediate

Duration: 1:20:58

Speaker: : Erin Dickie and Sejal Patel

This is a hands-on tutorial on PLINK, the open source whole genome association analysis toolset. The aims of this tutorial are to teach users how to perform basic quality control on genetic datasets, as well as to identify and understand GWAS summary statistics.

Difficulty level: Intermediate

Duration: 1:27:18

Speaker: : Dan Felsky

This is a tutorial on using the open-source software PRSice to calculate a set of polygenic risk scores (PRS) for a study sample. Users will also learn how to read PRS into R, visualize distributions, and perform basic association analyses.

Difficulty level: Intermediate

Duration: 1:53:34

Speaker: : Dan Felsky

This is a tutorial introducing participants to the basics of RNA-sequencing data and how to analyze its features using Seurat.

Difficulty level: Intermediate

Duration: 1:19:17

Speaker: : Sonny Chen

This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.

Difficulty level: Intermediate

Duration: 1:11:04

Speaker: : Etay Hay

This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment.

This lesson corresponds to slides 1-64 in the PDF below.

Difficulty level: Intermediate

Duration: 1:28:14

Speaker: : Andreea Diaconescu

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below.

Difficulty level: Intermediate

Duration: 1:15:04

Speaker: : Daniel Hauke

This lesson briefly goes over the outline of the Neuroscience for Machine Learners course.

Difficulty level: Intermediate

Duration: 3:05

Speaker: : Dan Goodman

This lesson delves into the the structure of one of the brain's most elemental computational units, the neuron, and how said structure influences computational neural network models.

Difficulty level: Intermediate

Duration: 6:33

Speaker: : Marcus Ghosh

Following the previous lesson on neuronal structure, this lesson discusses neuronal function, particularly focusing on spike triggering and propogation.

Difficulty level: Intermediate

Duration: 6:58

Speaker: : Marcus Ghosh

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